Critical factors associated with postpartum maternal death in Ethiopia

Background Globally most maternal deaths occur during the postpartum period; however, the burden is disproportionately higher in some Sub-Saharan African countries including Ethiopia. According to Ethiopian Ministry of Health’s annual report, in 2019 alone, nearly 70% of maternal deaths happen during the postpartum period. Although several studies have been conducted on postpartum maternal deaths in Ethiopia, most of the studies were focused either on individual-level or district-level determinants with limited emphasis on the timing of death and in relatively small and localized areas. Therefore, this study aimed at identifying the determinants of postpartum death both at an individual and districts level, which could shed light on designing pragmatic policies to reduce postpartum maternal death. Methods The study utilized secondary data obtained from the Ethiopian maternal death surveillance system. A total of 4316 reviewed maternal death from 645 districts of Ethiopia were included in the analysis. A multilevel multinomial logistic regression model was applied to examine factors significantly associated with postpartum maternal death in Ethiopia. Result The findings revealed that 65.1% of maternal deaths occurred during the postpartum period. The factors associated with postpartum death included previous medical history (history of ANC follow up and party), medical causes (obstetrics haemorrhage, hypertensive disorder of pregnancy, pregnancy-related infection, and non-obstetrics complication), personal factors (poor knowledge of obstetrics complication), and facility-level barriers (shortage of life-saving maternal commodities and delay in receiving treatment). Conclusion Almost seven in ten maternal deaths happen during the postpartum period. The rate was even higher for some women based on their previous medical history, level of awareness about obstetrics complication, medical conditions, as well as the readiness of the health facility at which the women was served. Since the postpartum period is identified as a critical time for reducing maternal death, policies and actions must be directed towards improving health education, ANC service utilization, and facility-level readiness.


Introduction
The source population for this study is all mothers who were deceased due to pregnancy and related complications and reviewed by the MDSR committee during the study period. Accordingly, a total of 4316 reviewed maternal death were included in the study. The death review was conducted by the established maternal death review committee at each health facility within the 645 districts of Ethiopia.

Death identification and reporting
Population under surveillance. All women of the reproductive age group in Ethiopia are eligible for the surveillance system.
Case definition The surveillance data is reported to the next level using pre-defined case definitions implemented in facility and community settings [53].

Investigation and verification of death.
After the death is distinguished using the above case definitions, it proceeds to the next step, which is the investigation and verification of death by the health extension workers at the community level; while, at a health facility level, public health emergency management (PHEM) focal person is responsible. Following that, a verbal autopsy is utilized to investigate and verify the community death. The investigation only proceeds after taking verbal consent from the family of the deceased woman. However, the facility-based abstraction format (FBAF) is utilized for facility deaths [53].
Review of death. Each completed verbal autopsy and facility-based maternal death abstraction must go through the review process by an established MDSR committee at the health facility. After the review, the death is reported to the next level using a case-based reporting format [53].
were included in the analyses. Potential predictors were then categorized into two groups: individual-level predictors and district-level predictor variables.
Individual factors such as age at death, education level, religion, history of antenatal followup, parity, place of death, the medical cause of death (MCOD), and the non-medical cause of death (only delay one) were included in the study. The MCOD was assigned using the standard world health organization (WHO) tool of the International statistical classification of diseases and related health problems, the tenth revision (ICD-10), which was adopted for deaths during pregnancy, childbirth, and the puerperium [55]. The assigned cause of death for each reviewed maternal death was grouped based on underlying causes of death during pregnancy, childbirth, and the puerperium which were mutually exclusive from one another. Moreover, delay one, which is associated with delay in seeking care, was measured by 5 item questioners that include: 1) visited a traditional healer or traditional birth attendant first 2) the family had insufficient money 3) lack of awareness of obstetric complications 4) nearest healthcare facility was more than 1 km away 5) lack of decision to health facility due to perceived poor quality of care at health service. Responses to delay one questions were binary recoded "1" for Yes and "0" for No.
District-level factors include residence (urban and rural), contextual regions, delay two, and delay three. The 11 regions of Ethiopia are delineated for administrative purposes, and in this study, they were re-categorized into three contextual regions: pastoralist, agrarian, and city (which were defined based on the cultural and socio-economic backgrounds of their population) [56]. Delay two, which is indicative of delay in reaching a healthcare facility, was assessed using 5 item questions that include: 1) poor road condition or terrain 2) long travel time from home to a healthcare facility (more than an hour) 3) cost of transportation 4) lack of transportation and 5) no healthcare facility in the area (takes more than one hour to reach the healthcare facility). Furthermore, delay three, which is suggestive of delay in receiving care at the healthcare facility, was assessed using 4 item questions: 1) long travel time from health facility to health facility (which is usually related to inadequate referral system described through unavailability of ambulances, lack of fuel, technical failure of the ambulance while in service, and the use of public transport) 2) shortage of equipment and supplies 3) delay in management (service provision) after admission (more than 30 min from the time of arrival to the time of being assessed or receiving treatment) and 4) wrong assessment of risk, wrong diagnosis, and treatment.

Data analysis
Descriptive analysis. Descriptive statistics were conducted for the individual and districtlevel variables and are reported as frequency and percentage. Furthermore, to examine the crude association between the individual and community-level factors separately with a distinct time of death, p-values were calculated using Pearson's chi-squared test. A p-value of less than 0.05 was set for the statistical significance of an association.
Multivariate multilevel analysis. Two-level mixed-effects logistic regression analyses were employed using STATA version 17. The surveillance data was hierarchical, i.e., deceased women were nested in reporting facilities, and similarly reporting facilities were nested in districts. As a result of the nature of the data, mothers within the same district may be more similar to each other than mothers in the rest of the country. By considering the clustering effect, initially, a bivariate two-level mixed-effects logistic regression analysis was done to assess the association between the independent variables and the dependent variable of the study. The overall categorical variables with a p-value of <0.25 at the bivariate two-level mixed-effect logistic regression analysis were included in the final model of the multivariate two-level mixed-effects multinomial logistic regression model, in which the relative risk ratio(RRR) with 95% confidence intervals were estimated to identify independent variables of time of death of the deceased women [57]. P-values less than 0.05 were employed to declare statistical significance. Fixed effect and random effect were calculated to assess the individual and district variations, respectively. For the multilevel multinomial logistic regression analysis, the Stata 'gsem' syntax was employed [58]. Thus, four models were utilized in this analysis, the empty model (model containing no factors), Model I (containing only individual factors), Model II (containing only districts factors), and Model III (both individual and district-level factors). The fitted model was: where, yij is the time of death for the deceased woman I who resided in district J. S is the outcomes (antepartum, intrapartum, and postpartum). X is the matrix of independent variables at both individual and district levels. β(s) is the effect size of each independent variable on the probability of women dying during antepartum and intrapartum μoj(s)is a constant term and cross-level interaction term. The intra-class correlation (ICC) was calculated as the proportion of the between cluster variation in the total variation: Where Var (Uoj) is community-level variance and p 2 � 3 is individual-level variance (VI) equal to 3.29. The ICC takes a value between 0 and 1 and a high ICC value indicates that neighbourhoods are important in understanding individual differences in postpartum [59]. The MOR is defined as the median value of the odds ratio between the area at highest risk and the area at the lowest risk when randomly picking out two areas and it depends directly on the area-level variance [60]. It can be calculated using the following formula: MOR ¼ ðexp ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi 2 X VarðUojÞX 0:6745Þ p � exp ð0:95 ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffiffi VarðUojÞ p Where Var (Uoj) is the district-level variance, and 0.6745 is the 75th centile of the cumulative distribution function of the normal distribution with mean 0 and variance 1. In this study, MOR shows the extent to which the individual probability of experiencing death during postpartum is determined by the residential area [59]. The PCV is used to measure the total variation attributed to individual-level factors and area-level factors in the multilevel model [59,61]. It was calculated using the following mathematical equation: Where Ve is the variance in antepartum and intrapartum in the empty model and Vmi is the neighbourhood variance in the subsequent model.
Model fit statistics. Akaike's information criterion (AIC) and Schwarz's Bayesian information criteria (BIC) were used to assess the goodness of fit and inform the selection of nested models (individual-and community-level models). The AIC and BIC values were compared in successive models and the model with the lowest value was selected as a best-fit model [

Selected background characteristics of maternal death reviewed facilities
The proportion of death during the postpartum period was estimated at 65.1%, during the antepartum period was 19.8% and during the intrapartum period was 15.0%. Among reported facilities, more than half (58.3%) of the deaths were reported from primary-level health care facilities, and 98.8% from public facilities. More than half (57.9%) of the reviewed maternal deaths were reported from Amhara and Oromia regions. Moreover, nearly half (49.3%) of the deaths were reviewed in 2016 and 2017, ( Table 1).

Sociodemographic characteristics of the deceased women
The proportion of women who died during the postpartum period was higher among women aged between 40-49 years (71.6%) compared to women who were aged between 10-19 years (60.2%). Women who resided in a rural area had a higher proportion of death during intrapartum (15.6%) compared to those who resided in an urban area (11.6%). The proportion of women who died during the antepartum period was higher among women with parity between 0 and 1 (22.2%) compared to those who had a parity above five (16.3%), ( Table 2).

The proportion of assigned cause of death for reviewed maternal death
The proportion of women who died during the postpartum period was higher among women deceased due to pregnancy-related infection (81.5%) as compared to women who died due to other causes of death. Women who died due to unanticipated complications of management had a higher proportion of death during the intrapartum period (25.0%) compared to women who died due to other causes. The proportion of women who died due to abortive outcomes of pregnancy was higher among women who died during the antepartum period (77.1%) as compared to the remaining cause of death, ( Table 3).

The proportion of contributing causes of death
The proportion of women who died during the postpartum period was much higher among women who were wrongly diagnosed and treated (72.9%) compared to other contributing factors. Women who died due to lack of transportation had a higher proportion of death during intrapartum (21.6%) compared to other contributing factors. The proportion of women who died due to lack of money for transportation was higher among women who were deceased during the antepartum period (25.6%) as compared to delay factors, (Table 4).

Factors associated with an antepartum time of death in Ethiopia among reviewed deaths
Women who died in transit were more likely to die during the antepartum period compared to those who died at home [RRR = 1.94,95% CI:(1.38-2.71)]. Women who had a parity of more than five were less likely to die during the antepartum period compared to those who were nulliparous [RRR = 0.74; 95% CI:(0.58-0. 94)]. Women who died as a result of  (Table 5). Multilevel analysis (random-effects analysis). Table 6 presents quantities based on random effects. The status of time of death varied across districts (τ 2 = 0.47, p = <0.001). The empty model revealed that 12.3% of the total variance in time of death was accounted for by between-cluster variation of characteristics (ICC = 0.123). The district variability declined over successive models, from 12.3% in the empty model to 10.6% in the individual-level only model, 9.9% in the districts-level only model, and 9.6% in the final (combined) model. The proportional change in variance indicated that the addition of predictors to the empty model explained an increased proportion of variation in the time of death. Similar to ICC values, the combined model showed a higher PCV, i.e., 46% of the variance in the status of time of death could be explained by the combined factors at the individual and community levels. Furthermore, the MOR confirmed that the time of death was attributed to district-level factors. The MOR for a time of death was 1.90 in the empty model, which indicated the presence of variation between districts for a time of death since MOR was nearly two times higher than the reference (MOR = 1). The unexplained districts' variation for time of death decreased to a MOR of 1.75 when all factors were added to the empty model. This indicates that 0.25 (25%) of the heterogeneity was explained by both individual and community level factors, but still, there is a residual effect that is not explained by individual and district-level variables at the final full  model (MOR = 1.75). This implies that even though individual-and district-level factors were considered, the effect of clustering is still statistically significant in the full model. Table 6 (model fit statistics), the values of AIC and BIC showed subsequent reduction which indicates each model represents a significant improvement over the previous model and it points to the goodness of fit of the final model built in the analysis.

Discussion
The study observed the hierarchal effect of various factors that determine the timing of death among reviewed maternal deaths in Ethiopia. Our study has demonstrated that the timing of maternal death is determined by both individual factors (previous medical history, medical cause, and knowledge of obstetrics complications) and districts level factors (lack of transportation, shortage of life-saving maternal commodities in a health facility, and delay in receiving treatment).
Women's parity was positively associated with postpartum maternal death. This finding was comparable with studies done in Tanzania  . The possible justification might be related to pre-existing health conditions of the women, as well as access and utilization of maternity services [67, 68] and the combination of these factors may affect the outcome of the mother after delivery. In agreement with this, women of advanced maternal age, who are believed to have a relatively high parity are at an increased risk of maternal death. Per the recent Ethiopian demographic and health survey, only 18%,36%, and 40% of women of advanced maternal age used contraceptives; attended more than 4 ANC visits; and delivered at a health facility, respectively [52]. These findings imply that more improvement in the access and utilization of maternal service is particularly needed for multipara women. This should be coupled with adequate service provision to reduce maternal death during the postpartum period.
History of ANC visit was also one of the individual-level factors significantly associated with postpartum maternal death. Women who had at least one ANC visit were less likely to die during the antepartum and intrapartum periods. In line with this, the study also revealed that nearly 70% of the women were deceased at health facilities after delivery. This partly explains that attending ANC service encourages institutional delivery; however, it is not a guarantee for obtaining quality maternity service [69]. The uptake of the service may be compromised by various factors including the educational status of the women, the residence of the women, distance from the facility, and previous history of successful home delivery [70][71][72][73]. This suggests that the integration of ANC services with other maternal services and programs should be a priority to meet the ultimate objective of the ANC service. Meanwhile, improvement in the managing capacity of obstetrics emergencies should be the other priority to reduce postpartum maternal death significantly.
Among individual factors, the medical cause of death-both direct (HDP, obstetrics haemorrhage, and pregnancy-related infection) and indirect (non-obstetrics complication)-were positively associated with maternal death after delivery. The finding was concurrent, in the case of obstetrics haemorrhage, with studies conducted in Ethiopia (Debere-Tabore, Yirgalem, and Harir) [74][75][76], Nigeria [77], South Africa [78], and Mozambique [79]. The possible reason might be related to the management of the third phase of labour, which is provided by a trained health professional at a health facility. Ethiopia has put in place various measures to tackle the burden of obstetrics haemorrhage. Some of the measures that were put in place include the utilization of NASG during referral [80], provision of Misoprostol at the community level [81]; construction of mattering waiting room [14], and augmentation of compressive emergency obstetrics care (establishment of mini blood band and expansion of caesarean providing facility) [25,82]. Despite all these efforts, due to poor utilization of innovative technologies such as the use of uterine balloon tamponade, and simplified dosing of magnesium sulfate [25,83], postpartum haemorrhage remains unacceptably high in Ethiopia [25]. Overall, the result suggests that there is a huge gap in early detection, response, and treatment of postpartum haemorrhage.
As stated above HDP was also positively related to postpartum maternal death in Ethiopia. The finding was consistent with studies conducted in Ethiopia (Hawwasa, Yirgalem, and Hossana) [84], Nigeria [85], Turkey [86], Ghana [87], and Uganda [88]. The possible justification could be HDP's strong relation to the delay in seeking care and the delay in initiating anticonvulsant prophylaxis and antihypertensive drugs, which will result in irreversible damage to the brain because of intracranial haemorrhage. This result suggested the need for boosting the health system by providing innovative technologies, which improve proteinuria tests and blood pressure measurements [83]. In combination with efforts to enhance health-seeking behaviour, improving the referral system and availing essential drugs and supplies are required to reduce HDP during the postpartum period [89,90].
In addition, pregnancy-related infection also has a vital role in maternal death after delivery. This finding was aligned with studies done in 52 countries [91], including Uganda [92], and India [93]. The possible explanations that could contribute to the infection might be related to pre-delivery factors (pre-labour rupture of membranes, prolonged labours, multiple vaginal examinations (more than five), the health status of the women (anaemia, primiparity, and poor nutrition), and clinical procedures during delivery (episiotomy, caesarean section, and other invasive procedures). However, the outcome of the women is determined by the adequacy of vital signs assessment and early administration of antimicrobial therapy. Contrastingly, in the last twenty years, Ethiopia has declined maternal mortality due to infection by practising infection control measures [94]. Generally, the finding indicated that pregnancyrelated infection should be the other area of intervention since it is one of the major causes of maternal death in Ethiopia.
Furthermore, Non-obstetrics complication was also positively associated with postpartum maternal death. This finding is in line with studies done in Ethiopia (Jimma) [95] and Nigeria [37,96]. In Ethiopia, the leading indirect cause of maternal death was severe anaemia unrelated to haemorrhage [25]. The possible explanation for anaemia related death during the postpartum period relates to women's circulatory decomposition, which is manifested by increased cardiac output and decreased ability to blood loss, which ultimately results in shock and death [97]. On the other hand, the risk factors of anaemia such as low dietary intake of iron, intestinal or blood parasite infection, and chorine illness [98,99], could be handled during ANC visit by supplementation of iron, folic acid, and deworming.
Failure to recognize the complication of pregnancy is one of the individual factors related to postpartum maternal death. The finding of this study is parallel with studies done in Nigeria [100], Uganda [101], and Somalia [102]. The possible justification might be the fact that poor knowledge of obstetrics complications could make the women less prepared, which may have a negative consequence on accepting appropriate and timely referral to essential obstetric care. Similarly, knowledge of obstetric complications is influenced by educational status, the proximity of health facilities, and previous history of obstetric complications [103,104]. To address this challenge, Ethiopia introduced a health extension program in 2003, aiming to bring health knowledge and basic care directly to the households at a grassroots level [105]. However, as depicted in the result, further work must be done at the community level.
District-level factors such as scarcity of equipment and supplies and late management after admission were significantly associated with postpartum maternal death. The finding was congruent in case of shortage of equipment and supplies with studies done in Egypt [106], Tanzania [107], and Malawi [108]. This clearly shows that the shortage of blood and blood product is the main challenge in managing obstetrics complications in Ethiopia and other Sub-Saharan African countries [109,110]. To this effect, the establishment of a mini blood bank at Comprehensive Emergency Obstetric and New-born Care (CEmONC) facilities in Ethiopia was considered as a mitigation plan; despite that, its implementation was challenged by a low blood donation rate, and inadequate testing and quality monitoring capacity [4,25,110]. Overall, this result suggests that addressing the gap in availing essential life-saving maternal commodities (medicines, medical devices, and health supplies) should be the target area of intervention to reduce preventable postpartum maternal death.
Finally, late management of women after admission is also one of the district-level factors associated with maternal death after delivery. It is usually framed by a delay in waiting for treatment, which takes more than 30 minutes from the time of admission to assessment and receiving treatment [111]. A similar result was observed in studies done in Ethiopia (Addis Ababa and Tigray) [112,113], Mozambique [114], Brazil [115], and India [116]. This might be due to the reason for delays such as lack of essential maternal commodities, insufficient training, poor attitudes toward a patient, and poor facility infrastructure related to the operation room and surgical facilities. On the contrary, Ethiopia has designed an alternative strategy to augment the managing capacity of obstetrics emergencies at lower-level health facilities. To this effect, the country has introduced a training program for middle-level health care professionals called the Integrated Emergency Surgical Officers (IESO). Those professionals are trained to handle obstetrics emergencies at lower-level health facilities [117]. In addition, the country has invested resources to upgrade the existing health facilities [118]. However, despite all these efforts, facility-level barriers should still be considered as a milestone to achieve the aspired goal under SDG.
The study has limitations that need to be acknowledged. First, the data used for the analysis was secondary data with single-point time. Thus, only associations were examined, and it was impossible to confirm any causality. Second, all identified, confirmed, and reported maternal death through a weekly reporting system were not reviewed and sent via MDRF to the next level, which might introduce potential bias to the study. Third, nearly all deaths were reported and reviewed from public facilities with limited involvement of private health facilities, and this could affect the representativeness of the study. Fourth, a small number of maternal deaths were captured by the system, which is against national estimates and might compromise the inclusiveness of the study.

Conclusion
Overall, both individual and facility-level variables were significantly associated with postpartum maternal death among reviewed maternal deaths in Ethiopia. Thus, mothers with previous medical history (history of ANC follow up and party), medical cause (obstetrics haemorrhage, HDP, pregnancy-related infection, and non-obstetrics complication), personal factors (poor knowledge of obstetrics complications), and facility-level barriers (shortage of life-saving maternal commodities and delay in receiving treatment) were at increased risk of maternal death after delivery. The study corroborates that handling individual and districtlevel factors associated with postpartum maternal death through pragmatic policies and programs is essential to reducing maternal death after delivery. Therefore, emphasis should be given to encouraging the utilization of ANC service with more priority to higher parity women, so that they can get information on obstetric complications, birth preparedness, and complication readiness to prevent delay in seeking care, and improve early diagnosis and treatment of anaemia. Since the two medical causes of death (HDP and obstetrics haemorrhage) were key determinants for postpartum death in Ethiopia, it is better to enhance the application of innovative technologies, which are suitable for resource constraints settings. Accordingly, innovative services such as utilizing uterine balloon tamponade, improving proteinuria testing, and having better blood pressure measurements are recommended. As for the facility level barriers, further improvement in service delivery at health facilities should be the top priority.